What the data for this category looks like
Data for this category primarily originates from petition bureaus at all levels, departmental petition offices, subdistrict and township offices, and responsible unit offices. It includes standardized documents such as petition registration forms, acceptance notices, transfer and assignment letters, handling opinions, re-examination and review applications, and resolution plans. Data sources cover the national petition information system, local petition systems, the 12345 citizen hotline platform, grassroots governance platforms, and historical petition archives. Data is updated daily. Document structures are fixed, with core fields including request content, handling process, and policy basis. No additional custom non-standard fields are included.
What constraints these characteristics impose on the vector model and indexing link
Scattered data sources and daily incremental updates require the indexing process to support incremental synchronization and batch updates, to avoid performance losses caused by full reconstruction. Standardized documents have fixed fields, but minor format differences exist across source systems. Text formats must be unified before chunking to ensure consistent vector generation. Policy basis serves as the core content for Q&A, so priority must be given to ensuring the accuracy of vector recall for policy texts. The volume of data per indexing batch must also be controlled to avoid indexing timeouts caused by excessive data volume. Errors in petition data are directly linked to repeated petitions and petitions submitted to higher authorities than the authorized handling level. This requires ensuring accurate vector recall and avoiding omission of key policy bases.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with paragraph lengths of petition-related documents, covers complete policy basis segments |
| Overlap Length | 100–200 characters | Prevents policy basis segments from being truncated across chunks, preserves contextual semantic connections |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balances recall accuracy and coverage for petition policy Q&A |
| Single Search Limit | Top 5 results | Matches the limited number of policy references required for petition handling |
maxToken | 4000 token | Adapts to the context window limit of FastGPT v4.17.0, avoids exceeding model processing limits |
defaultToken | 1500 token | Ensures complete vector generation for single policy basis segments, meets input requirements of general embedding models |
Three common mistakes
- The knowledge base interface displays constant indexing status with no progress updates. Cause: No incremental indexing rules are configured. Full synchronization of daily updated petition data occupies excessive resources, causing the indexing process to block.
- Embedding model calls return a 503 status code, with a prompt that the text-embedding model under the current default group is unavailable. Cause: No available embedding model interface is bound on the FastGPT platform, or interface request frequency exceeds the quota.
- Generated vector results have large semantic deviations from the original policy text. Cause: Chunk length is set too small, truncating complete policy basis segments and preventing vectors from accurately matching semantic connections.
How to confirm proper configuration
- Manually upload a petition handling opinion document, and check if chunked text segments fully retain policy basis content.
- Initiate a policy-related test query, and verify that the number of returned search results matches the configured single search limit.
- Check embedding model call logs to confirm that
defaultTokenandmaxTokenparameter values meet configuration requirements. - Wait one full calendar day, and check if incremental indexing tasks complete automatically with no abnormal error logs.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.